org.apache.lucene.search.similarities

Class IBSimilarity

java.lang.Object

org.apache.lucene.search.similarities.Similarity

org.apache.lucene.search.similarities.SimilarityBase

org.apache.lucene.search.similarities.IBSimilarity

public class IBSimilarity
extends SimilarityBase

Provides a framework for the family of information-based models, as described in Stéphane Clinchant and Eric Gaussier. 2010. Information-based models for ad hoc IR. In Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval (SIGIR '10). ACM, New York, NY, USA, 234-241.

The retrieval function is of the form RSV(q, d) = ∑ -x^q_w log Prob(X_w ≥ t^d_w | λ_w), where

• x^q_w is the query boost;
• X_w is a random variable that counts the occurrences of word w;
• t^d_w is the normalized term frequency;
• λ_w is a parameter.

The framework described in the paper has many similarities to the DFR framework (see DFRSimilarity). It is possible that the two Similarities will be merged at one point.

To construct an IBSimilarity, you must specify the implementations for all three components of the Information-Based model.

1. Distribution: Probabilistic distribution used to model term occurrence
   • DistributionLL: Log-logistic
   • DistributionLL: Smoothed power-law
2. Lambda: λ_w parameter of the probability distribution
   • LambdaDF: Nw/N or average number of documents where w occurs
   • LambdaTTF: Fw/N or average number of occurrences of w in the collection
3. Normalization: Term frequency normalization

   Any supported DFR normalization (listed in DFRSimilarity)

See Also:

DFRSimilarity

WARNING: This API is experimental and might change in incompatible ways in the next release.

Nested Class Summary

Nested classes/interfaces inherited from class org.apache.lucene.search.similarities.Similarity

Similarity.SimScorer, Similarity.SimWeight

Field Summary

Fields

Modifier and Type	Field and Description
protected Distribution	distribution The probabilistic distribution used to model term occurrence.
protected Lambda	lambda The lambda (λw) parameter.
protected Normalization	normalization The term frequency normalization.

Fields inherited from class org.apache.lucene.search.similarities.SimilarityBase

discountOverlaps

Constructor Summary

Constructors

Constructor and Description
IBSimilarity(Distribution distribution, Lambda lambda, Normalization normalization) Creates IBSimilarity from the three components.

Method Summary

Methods

Modifier and Type	Method and Description
protected void	explain(Explanation expl, BasicStats stats, int doc, float freq, float docLen) Subclasses should implement this method to explain the score.
Distribution	getDistribution() Returns the distribution
Lambda	getLambda() Returns the distribution's lambda parameter
Normalization	getNormalization() Returns the term frequency normalization
protected float	score(BasicStats stats, float freq, float docLen) Scores the document doc.
String	toString() The name of IB methods follow the pattern IB <distribution> <lambda><normalization>.

Methods inherited from class org.apache.lucene.search.similarities.SimilarityBase

computeNorm, computeWeight, decodeNormValue, encodeNormValue, explain, fillBasicStats, getDiscountOverlaps, log2, newStats, setDiscountOverlaps, simScorer

Methods inherited from class org.apache.lucene.search.similarities.Similarity

coord, queryNorm

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

distribution

protected final Distribution distribution

The probabilistic distribution used to model term occurrence.

lambda

protected final Lambda lambda

The lambda (λ_w) parameter.

normalization

protected final Normalization normalization

The term frequency normalization.

Constructor Detail

IBSimilarity

public IBSimilarity(Distribution distribution,
            Lambda lambda,
            Normalization normalization)

Creates IBSimilarity from the three components.

Note that null values are not allowed: if you want no normalization, instead pass Normalization.NoNormalization.

Parameters:

distribution - probabilistic distribution modeling term occurrence

lambda - distribution's λ_w parameter

normalization - term frequency normalization

Method Detail

score

protected float score(BasicStats stats,
          float freq,
          float docLen)

Description copied from class: SimilarityBase

Scores the document doc.

Subclasses must apply their scoring formula in this class.

Specified by:

score in class SimilarityBase

Parameters:

stats - the corpus level statistics.

freq - the term frequency.

docLen - the document length.

Returns:

the score.

explain

protected void explain(Explanation expl,
           BasicStats stats,
           int doc,
           float freq,
           float docLen)

Description copied from class: SimilarityBase

Subclasses should implement this method to explain the score. expl already contains the score, the name of the class and the doc id, as well as the term frequency and its explanation; subclasses can add additional clauses to explain details of their scoring formulae.

The default implementation does nothing.

Overrides:

explain in class SimilarityBase

Parameters:

expl - the explanation to extend with details.

stats - the corpus level statistics.

doc - the document id.

freq - the term frequency.

docLen - the document length.

toString

public String toString()

The name of IB methods follow the pattern IB <distribution> <lambda><normalization>. The name of the distribution is the same as in the original paper; for the names of lambda parameters, refer to the javadoc of the Lambda classes.

Specified by:

toString in class SimilarityBase

getDistribution

public Distribution getDistribution()

Returns the distribution

getLambda

public Lambda getLambda()

Returns the distribution's lambda parameter

getNormalization

public Normalization getNormalization()

Returns the term frequency normalization